Appendix B. Methods to model carbon stocks and the potential for emissions reduction.

We modeled and mapped carbon stocks (and fluxes) and the potential for emissions reduction. We quantified total carbon stocks (including above ground biomass (AGB), below ground living biomass (BGB), necromass, and soil carbon; t C/ha). AGB carbon was allocated using a land cover proxy, and conversions to BGB and necromass estimated from AGB carbon using ratio factors drawn from the literature. Soil carbon was determined based on soil type (terrestrial peat, mineral or mangrove) and depth of (terrestrial) peat. Areas with less than 30 cm of peat were considered to overlay mineral fluvaquents and tropaquent (entisol) soils, and were assigned an average soil carbon value (375.24 t C·ha-1·m-1) (Wahyunto et al. 2004). Peat soils are often denser on the surface due to compaction (Kool et al. 2006), and particularly when the overall depth is shallow (Wahyunto et al. 2004). We therefore assigned a higher value, 980 t C·ha-1·m-1, for the first 30 cm of peat for shallow peat soils (Wahyunto et al. 2004). The remaining peat, 30 cm and deeper, was assigned 786.8 t C·ha-1·m-1, the average of fibric, hemic, and sapric peat values (Wahyunto et al. 2004). Mangroves were allocated a global average carbon value of 783.5 t C/ha and based on an average soil depth of 199.4 meters (Donato et al. 2011).

To model carbon flux we developed a novel process model of the effects of fire on vegetation and peat, harvest of wood products (HWP) which may occur through forestry activities or initial clearing under conversion to agriculture or oil palm, oxidation of peat in the absence of fire, and growth of plant biomass. Carbon fluxes were modeled over five land management regimes: smallholder agriculture, oil palm, forestry, conservation, and no active land management (Table B1).

Table B1. Model parameterization for each land use management regime.

Smallholder agriculture

Oil palm

Forestry

Conservation

No management

Description

All areas converted to smallholder agriculture.

All areas converted to oil palm.

Eligible harvest forest types in forestry rotation. For all non-harvested forest types, this option is analogous to the conservation scenario, i.e., is not harvested, and no fires.

Assumes no fire, no agriculture, and no forestry: all areas left to grow into forests in the absence of fire. The maximum emissions reduction possible.

If forested (>100 t C/ha) then drainage at 0.2 m, or if deforested drainage at 0.3 m, oxidation at 6.08 and 9.12 t C·ha-1·yr-1 respectively.

If forested (>100 t C/ha) then drainage at 0.2 m, or if deforested drainage at 0.3 m, oxidation at 6.08 and 9.12 t C·ha-1·yr-1 respectively.

If forested (>100 t C/ha) then drainage at 0.2 m, or if deforested drainage at 0.3 m, oxidation at 6.08 and 9.12 t C·ha-1·yr-1 respectively.

Peat loss in fire

15 cm

15 cm

30 cm

NA

30 cm

Harvest of HWP

If start AGB is higher than value for a logged forest + 25% of a conventional forestry harvest (91.4 t C/ha) then the amount in excess of a logged forest (85.5 t C/ha) is diverted to HWP.

If start AGB is higher than value for a logged forest + 25% of a conventional forestry harvest.

40 year rotation on harvest of eligible forest types, with harvest at 65 m3/ha. See above for definition of eligible harvest types and times).

Nil

Nil

The probability of fire was modeled using a generalized linear mixed effects (lme4::glmer, R package) model to allow for the partitioning of variance due to both fixed effects (environmental variables) and random effects (the year, to account for El Niño events) (Bates et al. 2012). The model included the following environmental variables: current AGB; MODIS fire hotspot data for the years 2000 to 2006, which included one major El Niño event; distance to rivers and artificial canals (log transformed); the potential forest type; and the presence of agriculture. The resulting model showed expected trends of reduced fire probability with increasing AGB, increasing distance from canals and rivers, agricultural management, and in river-riparian forests (which mainly exist on mineral soils). The strongest factor increasing fire probability was El Niño years, and fire-susceptible forest types on peat soils. We assumed that a fire event would consume 70 percent of the available AGB (IPCC GPG 2006), regardless of soil type or existing land management. Peat consumption by fire was assumed to be 30 cm depth in unmanaged land, and 15 cm depth in managed agricultural areas (Ballhorn et al. 2009), or the entire profile of peat if less than these thresholds.

Under the forestry model, eligible harvest types are either river riparian, or mixed swamp forest, are initially earmarked for harvest in that harvest round (according for the forestry model, which assumes harvest in yr 0 for extant forest, yr 20 for degraded forest with some remaining cover, and yr 40 for areas that have no remaining forest cover). Areas must also reach the criteria of a minimum viable harvest level, if not, they are carried over into the subsequent harvest round. Harvest at 65 m3/ha, which with a conversion factor of 0.725 t biomass per m3 equates to 47.125 t biomass or 23.56 t C/ha. The minimum harvest threshold (174.06 t Biomass; the AGB that forest must be at before being eligible for harvest) is this harvest amount, plus 30% of a harvest (which is assumed to be damaged during harvest and burnt immediately), and the amount that remains after harvest (equivalent to the "burnt forests and bare" category, 112.8 t biomass).

HWP were assumed to act as a temporary storage of carbon after which biomass was combusted, and were modeled as a time discounted value using a 1.4% social discount rate (Stern 2006) and standard half life time horizons (2 years for paper, 30 years for solid products) with proportional distribution to paper (25%), solid wood (75%), and waste products (8% of total, assumed to be burnt immediately), and timber left standing (Murdiyarso et al. 2010). This equates to a conversion factor of 0.737, i.e., if 100 t C is sent to HWP rather than burnt immediately, this equates to 73.7 t C rather than 100 t C burnt.

For each year without fire, harvest, or conversion, the vegetation in each cell experienced growth and there was a loss of carbon though peat oxidation, reflecting the continued impacts of drainage in the region. Peat oxidative loss was estimated by the water table depth (Hooijer et al. 2006) and this was assumed to be 20, 40, and 80 cm respectively for natural or restored, drained, and agricultural areas (Euroconsult Mott MacDonald et al. 2008). We used the average oxidative loss of 3 t C·ha-1·yr-1 for every additional 10 cm drainage depth (assuming an average of 50 percent carbon loss due to oxidation, rather than soil respiration; Couwenberg et al. 2010). While peatlands may accumulate carbon in soil, we did not include this as the water table levels are generally not conducive for peat growth in the region (Page et al. 2009). We did not consider carbon loss or accumulation in the saline peat soils of mangroves. The maximum AGB was assigned based on the potential forest type and expected land use (Table B2). Growth in AGB (in the absence of fire) up to a maximum allowed under the assumed land use and land cover was assumed to be 13.5 t dry biomass for up to 20 years and 3.7 t dry biomass thereafter (IPCC GPG 2006). The maximum peat available to be lost was assigned based on the carbon stock in the peat soils above mean sea level (at which burning and oxidation was assumed to cease).

Table B2. Maximum AGB carbon values.

Management

Type

MaxAGB (Mg C/ha)

Agriculture

Tree crops - palm

25.59

Tree crops - other

65.83

Agriculture - rubber mosaic

17.27

Agriculture - rice based, including sawah

14.65

Unmanaged

1 – Mangroves

137.4

2 – Low pole PSF

128.3

3 – River-Riparian

198.4

4 – Swamp forest (nipah)

181.9

5 – Mixed PSF

208.0

The model was run over a 100 year time period and the average and standard deviation for AGB and soil carbon was determined for each grid cell in each year. The change in AGB and total phytomass was calculated as the difference between the contemporary year, and the start year (2007). Soil carbon stocks were calculated as the initial stock minus the amount predicted to be lost each year. The carbon emitted and sequestered was converted to carbon dioxide equivalents (CO2e) using standard emission factors for each of the flux categories.

Literature Cited

Ballhorn, U., F. Siegert, M. Mason, and S. Limin. 2009. Derivation of burn scar depths and estimation of carbon emissions with LIDAR in Indonesian peatlands. Proceedings of the National Academy of Sciences of the United States of America 106:2121321218.

Murdiyarso, D., K. Hergoualc'h, and L. V. Verchot. 2010. Opportunities for reducing greenhouse gas emissions in tropical peatlands. Proceedings of the National Academy of Sciences of the United States of America 107:19655-19660.